교육·학술

Maps how parents and caregivers actually divide day-to-day responsibilities for a child's education — homework help, school communication, events, and decisions — and where the load feels unbalanced. Built for schools, family researchers, and co-parenting programs, with an AI follow-up that digs into the most recent moment the division felt unfair or broke down.

A research survey examining how AI tools are reshaping academic integrity norms among college and university students. Covers policy awareness, personal usage patterns, acceptability perceptions, peer behavior observations, and policy recommendations.

Measures how effectively an AI tutor supports student comprehension, engagement, and skill growth, for educators and edtech teams evaluating tutoring tools, with an AI follow-up interview that reconstructs a recent tutoring session to surface what helped or confused the student.

This survey evaluates student experiences with AI chatbot advising and support services. It measures usage patterns, task types, perceived success rates, satisfaction, trust, comparison to human advisors, and areas for improvement.

Assesses whether research participants understood key elements of informed consent—study purpose, risks, data handling, and participant rights. Designed for post-study administration to support IRB compliance and iterative improvement of consent materials.

An academic instrument measuring student awareness of, exposure to, and attitudes toward artificial intelligence ethics. Covers concept familiarity, training exposure, ethical dilemma responses, regulation views, and willingness to prioritize ethics over convenience. Estimated completion: 8–12 minutes.

Collects structured student feedback on instructor effectiveness, course materials, workload balance, and learning outcomes to inform end-of-term course improvements at the undergraduate or graduate level.

Measures student attitudes toward AI tool usage in academic settings, integrity concerns, and preferred institutional safeguards. Designed for higher-education administrators and faculty seeking data to inform responsible AI-use policies.

Measures how well a training class, private trainer, or app actually changed a pet's behavior — covering goals worked on, progress made, and satisfaction with instruction. An AI follow-up interview digs into the specific moment behavior clicked (or didn't) and what got in the way, for trainers and pet-training platforms who want more than a star rating.

교사의 명확성, 공정성, 참여 유도, 지원에 대한 학생 피드백을 수집하며, 학습에 가장 중요한 자질이 무엇인지도 파악합니다. AI 후속 인터뷰는 각 학생의 전반적인 추천 점수 이면에 있는 구체적인 순간을 파고들어, 모호한 칭찬이나 불만 대신 구체적인 사례를 드러냅니다.

Measures student perceptions of rubric clarity, fairness, and usefulness for learning. Designed for course designers and academic researchers seeking to improve grading transparency and rubric design.

Captures how students experience a teacher's clarity, fairness, feedback, and engagement using a rating battery and a best-worst trade-off on what matters most, then uses an AI follow-up interview to dig into the specific moment behind their lowest rating instead of a vague complaint. Built for course or semester-end teacher evaluations.

Gathers student feedback on a teacher's instructional clarity, fairness, availability, and classroom management, then uses an AI follow-up interview to unpack a specific moment behind the overall rating. Built for department chairs, instructional coaches, and school administrators running end-of-term teacher evaluations.

Captures how learners actually experienced an online training course — completion, content quality, instructor clarity, and confidence applying the material — for L&D teams and course designers. An AI follow-up interview digs into the real obstacles behind low satisfaction or recommendation scores instead of settling for a bare number.

Screens teaching candidates on certification, subject and grade-level fit, classroom experience, and job priorities before an in-person interview. Built for school and district hiring teams; the AI follow-up interview goes past rehearsed talking points to reconstruct how a candidate actually handled a real classroom challenge.

부모가 자녀의 안전에 대해 얼마나 안심하는지, 시설이 일상적인 소통을 얼마나 잘하는지, 교직원이 아이들에게 얼마나 따뜻하게 대하는지, 발달 활동이 연령에 적절하다고 느끼는지를 측정합니다. AI 후속 질문은 낮은 안전 또는 소통 점수 뒤에 있는 구체적인 사건이나 부족한 부분을 파고들어, 점수만으로는 놓칠 수 있는 세부 사항을 드러냅니다.

Measures how thoroughly and consistently school bus drivers complete pretrip inspections, what gets in the way, and how confident they are in catching defects before a route — with an AI follow-up that reconstructs a real recent inspection instead of relying on general self-reports. Built for transportation directors and safety managers auditing inspection culture.

Measures how effectively employees apply recent training to their jobs, identifying barriers, enablers, and perceived outcomes to improve learning transfer and training ROI.

Evaluates how well a training program delivered on content quality, instructor effectiveness, and real-world applicability, including a best-worst trade-off on what matters most in future sessions. An AI follow-up interview digs into whether participants have actually used the skills on the job and what got in the way if not. Built for L&D teams and training providers assessing course impact.

This survey assesses instructor preparedness to integrate artificial intelligence into teaching, covering technology proficiency, AI literacy, pedagogical confidence, institutional support, professional development needs, and workload concerns. Estimated completion time: 10 minutes.